Data Quality Management in the Data Age

This book addresses data quality management for data markets, including foundational quality issues in modern data science. By clarifying the concept of data quality, its impact on real-world applications, and the challenges stemming from poor data quality, it will equip data scientists and engineers with advanced skills in data quality management, with a particular focus on applications within data markets. This will help them create an environment that encourages potential data sellers with high-quality data to join the market, ultimately leading to an improvement in overall data quality.

High-quality data, as a novel factor of production, has assumed a pivotal role in driving digital economic development. The acquisition of such data is particularly important for contemporary decision-making models. Data markets facilitate the procurement of high-quality data and thereby enhance the data supply. Consequently, potential data sellers with high-quality data are incentivized to enter the market, an aspect that is particularly relevant in data-scarce domains such as personalized medicine and services.

Data scientists have a pivotal role to play in both the intellectual vitality and the practical utility of high-quality data. Moreover, data quality control presents opportunities for data scientists to engage with less structured or ambiguous problems. The book will foster fruitful discussions on the contributions that various scientists and engineers can make to data quality and the further evolution of data markets.

Contains numerous tutorials designed to enhance readers' understanding Introduces seven methods of experimental design Includes one dedicated chapter on managing Ghost data, preparing readers to collect high-quality data

Autorentext

Haiyan Yu is an Associate Professor at Chongqing University of Posts and Telecommunications (China). He obtained his Ph.D. from Tianjin University in 2015. Subsequently, he served as a Postdoctoral Fellow at the University of Electronic Science and Technology of China from 2016 to 2017, and at Pennsylvania State University (US) from 2017 to 2020. Additionally, he was a Visiting Scholar at Purdue University (US) from 2020 to 2021. His research interests include causal inference and machine learning, personalized medicine, quality management, constrained optimization, and clinical decision support systems.

Inhalt

Introduction of data quality management.- Quality management in Data Science.- Pillars of data quality management.- Tools of data quality management.- Experimental designs for data quality control.- High-quality data collection in data markets.- Ghost data in data quality management.- Summary.